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• W2968689095 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Deep learning for local seismic image processing: Fault detection, structure-oriented smoothing with edge-preserving, and slope estimation by using a single convolutional neural networkAuthors: Xinming WuLuming LiangYunzhi ShiZhicheng GengSergey FomelXinming WuBEG, UT AustinSearch for more papers by this author, Luming LiangMicrosoft Applied Science GroupSearch for more papers by this author, Yunzhi ShiBEG, UT AustinSearch for more papers by this author, Zhicheng GengBEG, UT AustinSearch for more papers by this author, and Sergey FomelBEG, UT AustinSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3215251.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe three seismic image processing tasks of fault detection, structure-oriented smoothing with edge preserving, and slope estimation are related to each other and all involve analyzing seismic structural features. In conventional processing schemes, however, these three tasks are often independently preformed by different algorithms and challenges remain in each of them. We propose to simultaneously perform all the three tasks by using a single convolutional neural network. To train the network, we automatically create thousands of 3D synthetic noisy seismic images and the corresponding ground truth of fault images, clean seismic images, and seismic normal vectors. Although trained with only synthetic datasets, the network automatically learns to accurately perform all the three image processing tasks in a general seismic image. Multiple field examples show that the network is significantly superior to the conventional methods in all the three tasks of computing a more accurate and sharper fault detection, a smoothed seismic volume with better enhanced structures and structural edges, and more accurate reflection slopes.Presentation Date: Monday, September 16, 2019Session Start Time: 1:50 PMPresentation Time: 4:45 PMLocation: 221DPresentation Type: OralKeywords: interpretation, artificial intelligence, machine learning, faults, seismic attributesPermalink: https://doi.org/10.1190/segam2019-3215251.1FiguresReferencesRelatedDetailsCited byAbsolute acoustic impedance inversion using convolutional neural networks with transfer learningShaoyong Liu, Wenjun Ni, Wenqian Fang, and Lihua Fu2 February 2023 | GEOPHYSICS, Vol. 88, No. 2An Intelligent MT Data Inversion Method With Seismic Attribute EnhancementIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Joint use of multiseismic information for lithofacies prediction via supervised convolutional neural networksMinghui Xu, Luanxiao Zhao, Shunli Gao, Xuanying Zhu, and Jianhua Geng1 July 2022 | GEOPHYSICS, Vol. 87, No. 5A MATLAB code package for 2D/3D local slope estimation and structural filteringHang Wang, Yunfeng Chen, Omar M. Saad, Wei Chen, Yapo Abolé Serge Innocent Oboué, Liuqing Yang, Sergey Fomel, and Yangkang Chen3 March 2022 | GEOPHYSICS, Vol. 87, No. 3Acoustic impedance inversion using convolutional neural network with transfer learningShaoyong Liu, Wenjun Ni, Wenqian Fang, and Lihua Fu24 February 2022Concealed-Fault Detection in Low-Amplitude Tectonic Area—An Example of Tight Sandstone Reservoirs13 October 2021 | Minerals, Vol. 11, No. 10Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults12 September 2021 | Journal of Geophysical Research: Solid Earth, Vol. 126, No. 9Self-supervised learning for low frequency extension of seismic dataMeixia Wang, Sheng Xu, and Hongbo Zhou30 September 2020 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Xinming Wu, Luming Liang, Yunzhi Shi, Zhicheng Geng, and Sergey Fomel, (2019), Deep learning for local seismic image processing: Fault detection, structure-oriented smoothing with edge-preserving, and slope estimation by using a single convolutional neural network, SEG Technical Program Expanded Abstracts : 2222-2226. https://doi.org/10.1190/segam2019-3215251.1 Plain-Language Summary Keywordsinterpretationartificial intelligencemachine learningfaultsseismic attributesPDF DownloadLoading ..." @default.
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• W2968689095 title "Deep learning for local seismic image processing: Fault detection, structure-oriented smoothing with edge-preserving, and slope estimation by using a single convolutional neural network" @default.
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